crtradeworks/gist:8784b4c46ab31e4b04a9

## gistfile1.json
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00128620478494\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import csv\n",
    "import math\n",
    "import numpy\n",
    "\n",
    "# Import data from csv files\n",
    "EURUSDreader = csv.reader(open('EURUSD.csv'))\n",
    "\n",
    "EURUSD = []\n",
    "for line in EURUSDreader:\n",
    "\tEURUSD.append(line)\n",
    "\n",
    "# Data processing\n",
    "EURUSD_Open = []\n",
    "for row in EURUSD:\n",
    "\tEURUSD_Open.append(float(row[2]))\n",
    "\n",
    "EURUSD_High = []\n",
    "for row in EURUSD:\n",
    "\tEURUSD_High.append(float(row[3]))\n",
    "\n",
    "EURUSD_Low = []\n",
    "for row in EURUSD:\n",
    "\tEURUSD_Low.append(float(row[4]))\n",
    "\n",
    "EURUSD_Close = []\t\n",
    "for row in EURUSD:\n",
    "\tEURUSD_Close.append(float(row[5]))\n",
    "\n",
    "EURUSD_Volume = []\n",
    "for row in EURUSD:\n",
    "\tEURUSD_Volume.append(float(row[6]))\n",
    "\n",
    "EURUSD_Return = []\n",
    "for index in (range(len(EURUSD_Close) - 1)):\n",
    "\ttemp = math.log(EURUSD_Close[index + 1]) - math.log(EURUSD_Close[index])\n",
    "\tEURUSD_Return.append(temp)\n",
    "\n",
    "sd_EURUSD = numpy.std(EURUSD_Return)\t\n",
    "\n",
    "\n",
    "print sd_EURUSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KrjcphiEmjeCR5GqXHtGF0aQesVU0i/Qx7B2W2wjg0/5vCy6zVX8TLi4Zx/kk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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22b9a62510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the volatility\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(EURUSD_Return, '-')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"0.00128620478494\n"
	]
	}
	],
	"source": [
	"%matplotlib inline\n",
	"import csv\n",
	"import math\n",
	"import numpy\n",
	"\n",
	"# Import data from csv files\n",
	"EURUSDreader = csv.reader(open('EURUSD.csv'))\n",
	"\n",
	"EURUSD = []\n",
	"for line in EURUSDreader:\n",
	"\tEURUSD.append(line)\n",
	"\n",
	"# Data processing\n",
	"EURUSD_Open = []\n",
	"for row in EURUSD:\n",
	"\tEURUSD_Open.append(float(row[2]))\n",
	"\n",
	"EURUSD_High = []\n",
	"for row in EURUSD:\n",
	"\tEURUSD_High.append(float(row[3]))\n",
	"\n",
	"EURUSD_Low = []\n",
	"for row in EURUSD:\n",
	"\tEURUSD_Low.append(float(row[4]))\n",
	"\n",
	"EURUSD_Close = []\t\n",
	"for row in EURUSD:\n",
	"\tEURUSD_Close.append(float(row[5]))\n",
	"\n",
	"EURUSD_Volume = []\n",
	"for row in EURUSD:\n",
	"\tEURUSD_Volume.append(float(row[6]))\n",
	"\n",
	"EURUSD_Return = []\n",
	"for index in (range(len(EURUSD_Close) - 1)):\n",
	"\ttemp = math.log(EURUSD_Close[index + 1]) - math.log(EURUSD_Close[index])\n",
	"\tEURUSD_Return.append(temp)\n",
	"\n",
	"sd_EURUSD = numpy.std(EURUSD_Return)\t\n",
	"\n",
	"\n",
	"print sd_EURUSD"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"image/png": 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KrjcphiEmjeCR5GqXHtGF0aQesVU0i/Qx7B2W2wjg0/5vCy6zVX8TLi4Zx/kk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\nYII=\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7f22b9a62510>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"# Plot the volatility\n",
	"import matplotlib.pyplot as plt\n",
	"plt.plot(EURUSD_Return, '-')\n",
	"\n",
	"plt.show()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"celltoolbar": "Raw Cell Format",
	"kernelspec": {
	"display_name": "Python 2",
	"language": "python",
	"name": "python2"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 2
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython2",
	"version": "2.7.6"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}